Video content (e.g., videos or motion pictures) often contains text that is readable by a user viewing the video content. For example, the text can include information such as a title, film rating, actor names, copyright infringement warning, film production credits, the names of products or businesses, and other textual information.
Existing systems attempt to provide accurate video content information in individual directories or databases. For example, some websites provide lists of actors and production credits for thousands of movies. However, the websites often rely on users of the websites and manual entry for the information, and therefore it is oftentimes inaccurate. Automated systems attempting to analyze the entire video to identify text yields unsatisfactory results. For example, analyzing each frame of a video is time-consuming and cost prohibitive, as a typical video with ninety minutes of content stored therein at thirty frames per second has 162,000 frames. Analyzing each frame therefore requires significant expense and time. Further, current optical character recognition (OCR) systems are not able to consistently recognize text that is interspersed over noisy backgrounds, such as graphics or images typically found in videos. For example, the contrast between the text and the rest of the image is often insufficient for the OCR system to distinguish between the text and the rest of the image.
In some systems, distributors or retailers of video content permit the user to view a short preview (e.g., the first two minutes) of the video prior to purchase. Text displayed at the beginning of the video (e.g., the copyright warning or opening credits) usually consumes an inordinate portion of the preview due to the preview's limited duration. Attempts to offset the beginning point of the preview by a set number of frames or period of time have proved unsuccessful as there is not an industry-wide standard number of frames or amount of time that the opening text is displayed.
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